GLM-5.2 vs Muse Spark 1.3
Muse Spark 1.3 leads the LLM Stats Score 55.1 to 45.6. Muse Spark 1.3 is 9.3x cheaper per token.
Zhipu AI · Meta · Updated for 2026
Which is better?
Muse Spark 1.3 leads the overall LLM Stats Score 55.1 to 45.6, ranking #4 overall.
In the 2 individual benchmarks reported for both models, Muse Spark 1.3 wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Muse Spark 1.3 is roughly 9.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose GLM-5.2
- you need open weights you can self-host or fine-tune
Choose Muse Spark 1.3
- overall performance matters — it scores 55.1 and ranks #4 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- cost matters — it's about 9.3x cheaper per token
- you want the most recent training data — it shipped Sep 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
19 reported for GLM-5.2 · 11 for Muse Spark 1.3
GLM-5.2 outperforms in 0 benchmarks, while Muse Spark 1.3 is better at 2 benchmarks (DeepSWE 1.1, Terminal-Bench 2.1).
Muse Spark 1.3 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5.2 ($0.75/1M tokens) is 7.5x more expensive than Muse Spark 1.3 ($0.10/1M tokens).
For output processing, GLM-5.2 ($2.40/1M tokens) is 12.0x more expensive than Muse Spark 1.3 ($0.20/1M tokens).
In conclusion, GLM-5.2 is more expensive than Muse Spark 1.3.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,048,576 tokens. GLM-5.2 can generate longer responses up to 1,048,576 tokens, while Muse Spark 1.3 is limited to 943,718 tokens.
Input capabilities
Documented input modalities across available providers
Muse Spark 1.3 supports multimodal inputs, whereas GLM-5.2 does not.
Muse Spark 1.3 can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5.2
Muse Spark 1.3
License
Usage and distribution terms
GLM-5.2 is licensed under MIT, while Muse Spark 1.3 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
GLM-5.2 was released on 2026-06-16, while Muse Spark 1.3 was released on 2026-09-02.
Muse Spark 1.3 is 3 months newer than GLM-5.2.
Jun 16, 2026
2 months ago
Sep 2, 2026
1 weeks ago
2mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-5.2 is available from DeepInfra, Fireworks, FriendliAI, Novita, Together, ZAI. Muse Spark 1.3 is available from Meta Model API.
GLM-5.2
Muse Spark 1.3
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.2 and Muse Spark 1.3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.2 vs Muse Spark 1.3.